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首页> 美国卫生研究院文献>Springer Open Choice >The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites
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The capabilities and limitations of conductance-based compartmental neuron models with reduced branched or unbranched morphologies and active dendrites

机译：具有减少的分支或不分支形态和活跃树突的基于电导的隔室神经元模型的功能和局限性

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Conductance-based neuron models are frequently employed to study the dynamics of biological neural networks. For speed and ease of use, these models are often reduced in morphological complexity. Simplified dendritic branching structures may process inputs differently than full branching structures, however, and could thereby fail to reproduce important aspects of biological neural processing. It is not yet well understood which processing capabilities require detailed branching structures. Therefore, we analyzed the processing capabilities of full or partially branched reduced models. These models were created by collapsing the dendritic tree of a full morphological model of a globus pallidus (GP) neuron while preserving its total surface area and electrotonic length, as well as its passive and active parameters. Dendritic trees were either collapsed into single cables (unbranched models) or the full complement of branch points was preserved (branched models). Both reduction strategies allowed us to compare dynamics between all models using the same channel density settings. Full model responses to somatic inputs were generally preserved by both types of reduced model while dendritic input responses could be more closely preserved by branched than unbranched reduced models. However, features strongly influenced by local dendritic input resistance, such as active dendritic sodium spike generation and propagation, could not be accurately reproduced by any reduced model. Based on our analyses, we suggest that there are intrinsic differences in processing capabilities between unbranched and branched models. We also indicate suitable applications for different levels of reduction, including fast searches of full model parameter space.Electronic supplementary materialThe online version of this article (doi:10.1007/s10827-010-0258-z) contains supplementary material, which is available to authorized users.
机译：基于电导的神经元模型经常用于研究生物神经网络的动力学。为了提高速度和易于使用，通常会降低这些模型的形态复杂性。简化的树状分支结构与全分支结构的输入处理方式可能不同，因此可能无法重现生物神经处理的重要方面。尚不清楚哪种处理能力需要详细的分支结构。因此，我们分析了完整或部分分支的简化模型的处理能力。这些模型是通过折叠苍白球（GP）神经元完整形态模型的树突状树而创建的，同时保留其总表面积和电声长度以及其被动和主动参数。将树状树倒塌成单根电缆（无分支模型），或保留分支点的全部补足（分支模型）。两种减少策略都使我们能够使用相同的通道密度设置来比较所有模型之间的动力学。两种简化模型通常都保留了对体细胞输入的完整模型响应，而与未分支的简化模型相比，通过分支可以更紧密地保留树突状输入响应。但是，任何简化模型都无法准确地再现受局部树突状输入电阻强烈影响的特征，例如主动树突状钠尖峰的生成和传播。根据我们的分析，我们建议直链模型和分支模型之间的处理能力存在内在差异。我们还指出了适用于不同简化级别的应用，包括快速搜索整个模型参数空间。电子补充材料本文的在线版本（doi：10.1007 / s10827-010-0258-z）包含补充材料，可授权使用用户。

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